Evaluation of inherent performance of intelligent medical decision support systems: utilising neural networks as an example

Authors:
A. E. Smith;C. D. Nugent;S. I. McClean
Affiliations:
Medical Informatics, Faculty of Informatics, University of Ulster, Jordanstown, Newtownabbey, Antrim BT37 0QB, Northern Ireland, UK;Medical Informatics, Faculty of Informatics, University of Ulster, Jordanstown, Newtownabbey, Antrim BT37 0QB, Northern Ireland, UK;Medical Informatics, Faculty of Informatics, University of Ulster, Jordanstown, Newtownabbey, Antrim BT37 0QB, Northern Ireland, UK
Venue:
Artificial Intelligence in Medicine
Year:
2003

Citing 9
Cited 5

Use of neural networks in predicting the risk of coronary artery disease

Computers and Biomedical Research
Classification of arrhythmic events in ambulatory electrocardiogram, using artificial neural networks

Computers and Biomedical Research
Neural networks and logistic regression: Part II

Computational Statistics & Data Analysis
Artificial neural networks compared to factor analysis for low-dimensional classification of high-dimensional body fat topography data of healthy and diabetic subjects

Computers and Biomedical Research
Artificial Neural Networks in Biomedicine

Artificial Neural Networks in Biomedicine
Some considerations for the implementation of knowledge-based expert systems

ACM SIGART Bulletin
Multi-layer neural network analysis of cerebrospinal fluid pressure patterns in idiopathic normal-pressure hydrocephalus

Technology and Health Care
Paper: On the quality of neural net classifiers

Artificial Intelligence in Medicine
The evaluation of expert diagnostic systems - How to assess outcomes and quality parameters?

Artificial Intelligence in Medicine

Do physicians value decision support? a look at the effect of decision support systems on physician opinion

Artificial Intelligence in Medicine
A decision support system for lower back pain diagnosis: uncertainty management and clinical evaluations

Decision Support Systems
Towards a framework for healthcare simulation

Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Introducing intelligence in electronic healthcare systems: state of the art and future trends

Artificial intelligence
Improving Bayesian credibility intervals for classifier error rates using maximum entropy empirical priors

Artificial Intelligence in Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Researchers who design intelligent systems for medical decision support, are aware of the need for response to real clinical issues, in particular the need to address the specific ethical problems that the medical domain has in using black boxes. This means such intelligent systems have to be thoroughly evaluated, for acceptability. Attempts at compliance, however, are hampered by lack of guidelines. This paper addresses the issue of inherent performance evaluation, which researchers have addressed in part, but a Medline search, using neural networks as an example of intelligent systems, indicated that only about 12.5% evaluated inherent performance adequately. This paper aims to address this issue by concentrating on the possible evaluation methodology, giving a framework and specific suggestions for each type of classification problem. This should allow the developers of intelligent systems to produce evidence of a sufficiency of output performance evaluation.